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Ranking tensors is simply the number of directions required to describe the properties of an object, meaning the dimensions of the array
contained in the tensor itself. For instance, a scalar doesn’t have any direction and, thus it is ranked by 0 tensor, while a vector, which can be described using only one direction so that it becomes a first rank tensor. The next object, which is a matrix, requires two directions to describe it and thus becomes a second rank tensor.

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